Course detail

# Applied Analytical Statistics

Students will gain knowledge of random variable, mathematical statistics, categorical analysis, methods of regression analysis and analysis of time series describing economics and social events.

Language of instruction

English

Number of ECTS credits

6

Mode of study

Not applicable.

Offered to foreign students

Of all faculties

Learning outcomes of the course unit

Students acquire basic knowledge of random variables and important types of their distribution, processing data sets of quantitative and qualitative character, point and interval estimation, the most widely used parametric tests and tests of goodness of fit, simple and complex indices, linear and nonlinear regression models and analysis of time series, and will be able to use this knowledge in real business environment so that they are able to receive relevant information needed to support the management of business activities.

Prerequisites

Fundamentals of linear algebra (sets, set operations), mathematical analysis (derivation, integral, combinatorials scheme) and probability theory (probability, conditional probability).

Co-requisites

Not applicable.

Planned learning activities and teaching methods

Teaching consists of lectures that have an explanation of basic principles and methodology of the discipline, practical problems and their sample solutions.

Exercise promote the practical knowledge of the subject presented in the lectures.

Assesment methods and criteria linked to learning outcomes

The course-unit credit is awarded on the following conditions (max. 40 points):
- elaboration of semestral assignments.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to theoretical questions within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semestral assignments,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

A (100–90), B (89–80), C (79–70), D (69–60), E (59–50), F (49–0).

COMPLETION OF THE COURSE FOR STUDENTS WITH INDIVIDUAL STUDY

The course-unit credit is awarded on the following conditions (max. 40 points):
- elaboration of semestral assignments.

The exam (max. 60 points)
- has a written form.
In the first part of the exam student solves 4 examples within 100 minutes. In the second part of the exam student works out answers to theoretical questions within 15 minutes.

The mark, which corresponds to the total sum of points achieved (max 100 points), consists of:
- points achieved in semestral assignments,
- points achieved by solving examples,
- points achieved by answering theoretical questions.

A (100–90), B (89–80), C (79–70), D (69–60), E (59–50), F (49–0).

Course curriculum

1. Discrete and continuous random variable (basic concepts, empirical and function characteristics)
2. Important type of distributions (Binomial distribution, Poission distribution, Gauss distribution, Exponential distribution...)
3. Bivariate random variables (correlation)
4. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function)
5. Data sample analysis
6. Parameters’ estimation (point and interval estimates)
7. Test of statistical hypothesis (basic concepts and procedure)
8. Basic parametric tests (t-test, F-test, ANOVA)
9. Index analysis
10. Individual and composite indexes
11. Linear regression model (basic concepts, the least square method)
12. Non-linear regression model (linearizable and non-linearizable regression models)
13. Time series analysis (basic characteristics, decomposition)

Work placements

Not applicable.

Aims

Students will be introduced to basic concepts of discrete and continuous random variables and their major distribution, processing data files, point and interval estimation, hypothesis testing, linear and nonlinear regression models and time series analysis. Students will be able to use appropriate methods in dealing with informatics and economic problems. After completing the course, students will be prepared to practically apply these methods in ICT and related economic subjects using statistical programs.

Specification of controlled education, way of implementation and compensation for absences

Attendance at lectures is not mandatory but is recommended. Attendance at seminars is controlled.

Recommended optional programme components

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

FIELD, A., J. MILES and Z. FIELD. Discovering Statistics Using R. 1 edition. Los Angeles, Calif.: SAGE Publications Ltd., 2012. ISBN 978-1-4462-0046-9. (EN)
MATHEWS, P. Design of Experiments with Minitab. Milwaukee: ASQ Quality Press, 2005. ISBN 978-08-738-9637-5. (EN)

KARPÍŠEK, Z. a M. DRDLA. Applied Statistics. Brno University of Technology, Faculty of Business and Management. Brno, 1999. ISBN 80-214-1493-6. (EN)
BOX, George E. P., William Gordon HUNTER a J. Stuart HUNTER, 1978. Statistics for experimenters: an introduction to design, data analysis, and model building. B.m.: Wiley. ISBN 978-0-471-09315-2. (EN)
MONTGOMERY, Douglas C., 2008. Design and Analysis of Experiments. B.m.: John Wiley & Sons. ISBN 978-0-470-12866-4. (EN)

eLearning

Classification of course in study plans

• Programme BAK-Z Bachelor's

branch BAK-Z , 1. year of study, summer semester, elective

• Programme BAK-E Bachelor's

branch BAK-ESBD , 1. year of study, summer semester, compulsory

• Programme BAK-ESBD Bachelor's, 2. year of study, summer semester, compulsory

#### Type of course unit

Lecture

13 hours, optionally

Teacher / Lecturer

Syllabus

1. Discrete and continuous random variable (basic concepts, empirical and function characteristics)
2. Important type of distributions (Binomial distribution, Poission distribution, Gauss distribution, Exponential distribution...)
3. Bivariate random variables (correlation)
4. Descriptive statistics (basic concepts, empirical characteristics, empirical distribution function)
5. Data sample analysis
6. Parameters’ estimation (point and interval estimates)
7. Test of statistical hypothesis (basic concepts and procedure)
8. Basic parametric tests (t-test, F-test, ANOVA)
9. Index analysis
10. Individual and composite indexes
11. Linear regression model (basic concepts, the least square method)
12. Non-linear regression model (linearizable and non-linearizable regression models)
13. Time series analysis (basic characteristics, decomposition)

Exercise

26 hours, optionally

Teacher / Lecturer

Syllabus

The content of the exercises corresponds to the content of the lectures.

eLearning